






"""simple docstring"""


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser(
		    description=(
		        'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
		        ' Distillation'
		    )
		)
		parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
		parser.add_argument('--model_name', default='roberta-large', type=str)
		parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
		parser.add_argument('--vocab_transform', action='store_true')
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()

		if args.model_type == "roberta":
				__SCREAMING_SNAKE_CASE   =   RobertaForMaskedLM.from_pretrained(args.model_name)
				__SCREAMING_SNAKE_CASE   =   'roberta'
		elif args.model_type == "gpt2":
				__SCREAMING_SNAKE_CASE   =   GPTaLMHeadModel.from_pretrained(args.model_name)
				__SCREAMING_SNAKE_CASE   =   'transformer'

		__SCREAMING_SNAKE_CASE   =   model.state_dict()
		__SCREAMING_SNAKE_CASE   =   {}

		# Embeddings #
		if args.model_type == "gpt2":
				for param_name in ["wte.weight", "wpe.weight"]:
						__SCREAMING_SNAKE_CASE   =   state_dict[F"""{prefix}.{param_name}"""]
		else:
				for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
						__SCREAMING_SNAKE_CASE   =   F"""{prefix}.embeddings.{w}.weight"""
						__SCREAMING_SNAKE_CASE   =   state_dict[param_name]
				for w in ["weight", "bias"]:
						__SCREAMING_SNAKE_CASE   =   F"""{prefix}.embeddings.LayerNorm.{w}"""
						__SCREAMING_SNAKE_CASE   =   state_dict[param_name]

    # Transformer Blocks #
		__SCREAMING_SNAKE_CASE   =   0
		for teacher_idx in [0, 2, 4, 7, 9, 11]:
				if args.model_type == "gpt2":
						for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
								for w in ["weight", "bias"]:
										__SCREAMING_SNAKE_CASE   =   state_dict[
										    F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
										]
						__SCREAMING_SNAKE_CASE   =   state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
				else:
						for layer in [
						    "attention.self.query",
						    "attention.self.key",
						    "attention.self.value",
						    "attention.output.dense",
						    "attention.output.LayerNorm",
						    "intermediate.dense",
						    "output.dense",
						    "output.LayerNorm",
						]:
								for w in ["weight", "bias"]:
										__SCREAMING_SNAKE_CASE   =   state_dict[
										    F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
										]
				std_idx += 1

		# Language Modeling Head ###s
		if args.model_type == "roberta":
				for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
						__SCREAMING_SNAKE_CASE   =   state_dict[F"""{layer}"""]
				if args.vocab_transform:
						for w in ["weight", "bias"]:
								__SCREAMING_SNAKE_CASE   =   state_dict[F"""lm_head.dense.{w}"""]
								__SCREAMING_SNAKE_CASE   =   state_dict[F"""lm_head.layer_norm.{w}"""]
		elif args.model_type == "gpt2":
				for w in ["weight", "bias"]:
						__SCREAMING_SNAKE_CASE   =   state_dict[F"""{prefix}.ln_f.{w}"""]
				__SCREAMING_SNAKE_CASE   =   state_dict['lm_head.weight']

		print(F"""N layers selected for distillation: {std_idx}""")
		print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

		print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
		torch.save(compressed_sd, args.dump_checkpoint)







"""simple docstring"""


import itertools
import math


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5     ,      int(math.sqrt(a__ ) + 1 )     ,      6 ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :str						=			2
						while True:
												if is_prime(a__ ):
																		yield num
												num += 1


def        UpperCAmelCase      (					a__ = 1_00_01 ):





						'''simple docstring'''






						return next(itertools.islice(prime_generator()     ,      nth - 1     ,      a__ ) )


if __name__ == "__main__":
		print(F"""{solution() = }""")